321

CONTRACTUAL AGRICULTURE: BETTER PARTNERSHIPS BETWEEN

SMALL FARMERS AND THE BUSINESS SECTOR IN THE SULTANATE OF

OMAN

Badria Hamed Al

Ruqishi a

Tarig Gibreel a

Faical Akaichi b

Lokman Zaibet a

Slim Zekri a

a Department of Natural Resource Economics, College of Agricultural and

Marine Sciences, Sultan Qaboos University, Muscat, Sultanate of Oman

b Department of Rural Economy, Environment and Society, Scotland's Rural

College, Edinburgh, United Kingdom

alruqishi80@yahoo.com (Corresponding author)

ARTICLE HISTORY:

Received: 28-Feb-2020

Accepted: 04-May-2020

Online Available: 01-Jun-

2020

Keywords:

Contract farming,

Stated preferences,

Transaction costs,

Discrete choice,

Mixed logit model,

Latent class model

ABSTRACT

This study aims to elicit the preferences of Omani small-scale vegetable

farmers towards contracting with different emerging and other potential

business partners along with other contract terms. To accomplish this, a

discrete choice experiment was adopted to elicit farmers' preferences

towards different contract models. The choice data was analyzed using

both latent classes as well as mixed logit models and as a result, three

classes were found to best capture the preferences. Class 1 represents 45%

of the respondents who are more likely to adopt the “multipartite

contract” model. This segment is characterized by a high education level,

older age, and smaller family size. The second class represents 31% of the

sample and tends to adopt an “informal contract” model. This group has a

low experience, larger farm size, and own their private land. The last class

represents 23% of the observations and is in favour of the “centralized

contract” model. The main characteristics of this class are the low

education level, older age, and medium income. Realizing the farmers’

preferences and their characteristics are certainly important in improving

farmers’ participation in the vegetables’ market and gearing the policies

towards food security.

Contribution/ Originality

The significance of this research stems from the genuine lack of research in Oman related to the role of

contractual arrangements involving different governance structures like the form of a true agribusiness

and farmer’s organization. Secondly, we add a new dimension to the literature by analysing farmers’

preferences for different contract models by using a latent class Model and categorizing the choices with

the different farmer’s characteristics.

DOI: 10.18488/journal.1005/2020.10.1/1005.1.321.335

ISSN (P): 2304-1455/ISSN (E):2224-4433

How to cite: Badria Hamed Al Ruqishi, Tarig Gibreel, Faical Akaichi, Lokman Zaibet, and Slim Zekri

(2020). Contractual agriculture: better partnerships between small farmers and the business sector in the

Sultanate of Oman. Asian Journal of Agriculture and Rural Development, 10(1), 321-335.

© 2020 Asian Economic and Social Society. All rights reserved.

Asian Journal of Agriculture and Rural Development Volume 10, Issue 1 (2020): 321-335

http://www.aessweb.com/journals/5005

Asian Journal of Agriculture and Rural Development, 10(1)2020: 321-335

322

1. INTRODUCTION

According to statistics from the Food and Agriculture Organization of the United Nations (FAO),

the Sultanate of Oman relies very heavily on imports to secure roughly 60% of its vegetable’s

needs (FAO, 2017). Food prices fluctuations and the need to be secured for food supplies dictate

the urgent need of having a robust food security policy. Food security-oriented studies in Oman

focused on production efficiency and overlooked the marketing aspect, hence this study has come

to close this gap. This research views farmer’s ability to grow depending upon better access to

high-value chains by participating in different contracts that are patronized by the partnership of

different potential business models. These models have emerged as a result of various measures

that have been taken by the government to ensure food security by focusing on the potential of

vegetable production within Oman. This has resulted in harnessing the private sector by

establishing an agribusiness company that could act as an institutional solution to improve the

efficiency of the sector. Moreover, small-scale farmers started to operate collectively through

farmers’ organizations. Over the years, contract farming has been well-thought-out as a system that

has great potential for providing an approach to integrate small-scale farmers into export,

processing markets and the food security (Dedehouanou et al., 2013; Reardon and Timmer, 2014;

Mishra et al., 2018, Vilbert et al., 2019; Gelli, 2015).

It is one of the institutional options that enable small farmers’ diversification by enhancing their

access to markets, decreasing price risks and transaction costs; contracts that deliver credit, inputs,

technology, extension services, and information, aid farmers increase production efficiency; build

profit-making culture, and increase income and employment (Birthal et al., 2008; Eaton and

Shepherd, 2001; Michelson, 2013; Da Silva and Rankin, 2013). Bellemare and Lim (2018) have

viewed the positive effects of contract farming in enhancing small-scale farmers’ market

participation and its positive impact on their welfare (Wang and Kooten, 2018). Moreover, it

enables farmers to upgrade their production value chain and tap into high-quality markets (Demont

and Rutsaert, 2017). Several authors also have found that engaging in contract farming has

improved small-scale farmers’ income and other studies have examined the impact of contract

farming on high-value crops like vegetables (see Otsuka et al., 2016 and Wang et al., 2014). It has

also been proven that contracts with different patrons such as private companies, processors,

cooperatives, and international companies can improve farmers’ income (Hazell et al., 2007).

Hence, contract farming has been promoted as an institutional innovation and as a critical element

for rural development to improve agricultural performance in developing countries.

Consequently, contract farming can be defined as a profitable relationship between a firm and a

group of farmers. Grosh (1994) noted that contract farming could act as an agro-institution that can

overcome market failure resulting from uncertainty and risk. Most of the studies (Castaneda et al.,

2018; Chamberlain and Anseeuw, 2017; Mishra et al., 2016) rarely focused on the complexity of

the set-up of such arrangements that involve different instruments like collective organization and

agribusiness companies. Different contract models may be perceived differently and these different

views depend on the type of contractor, the price, the length of the contract, and by the

characteristics of the respondents, Bellemare and Lim (2018). Accordingly, contract farming takes

many forms, such as the centralized, informal, and multipartite model (Eaton and Shepherd, 2001).

Contract farming models depend on four main components which are the type of contractor, the

type of product, the intensity of vertical integration, and the involved stakeholder’s number

(Bijman, 2008).

Since one size doesn't fit all (Gregory and Chapman, 2002), different potential contracts were tested

including an agribusiness company, farmers organization, and retailing companies. However,

researchers believe that farmers are still concerned about the contract’s nature and hence there is a

need to elicit their preferences towards those trade-offs. Since farmers weigh the benefits over the

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costs to maximize their utility, farmers face various kinds of opportunity costs before they decide

to engage in a specific contract model. Contract attributes are best analyzed by considering the

trade-offs between returns and risks associated with such a mutual relationship. The terms of the

contract and its levels were selected based on transaction cost theory (Seng, 2017).

Discrete Choice Experiment (DCE) has been used in many areas to measure preferences for choice

alternatives, for instance, DCE has been used to derive farmers’ preferences for contract and market

channel characteristics (Blandon et al., 2009; Schipmann and Qaim, 2011; Landmann et al., 2018;

Fischer and Wollni, 2018; Gelaw et al., 2016; Ochieng et al., 2017; Van den Broeck et al., 2017).

This study uses a DCE that is based on stated preference data. The stated choice method has

initially been developed in marketing research and applied in the contract (Lajili et al., 1997). This

data is collected from the farm households to analyze farmers' preferences for contract farming that

allows controlling the correlation between different attributes for different contract profiles. The

primary purpose of the stated preference approach is to assess these models by eliciting farmer’s

stated preferences towards hypothetical settings (Arouna et al., 2017). This methodology indicates

the maximum utility of a particular contract model by separately estimating the preferences of the

farmers for the pertinent attributes which characterize the contract model. The stated preference

approach is used given the limitation of real contracts on vegetable production in the study area.

Hence, the stated choice method offers an excellent opportunity to estimate the demand for new

potential contracts.

The objective of this paper is to analyze the farmer’s preferences towards different contract

structures in the selected study area in the Sultanate of Oman. This research examined different

contract options using a latent class model to enable policymakers and the emerging agribusinesses

in the vegetable industry to develop a suitable governance structure of contract farming that copes

with the business purpose of different farmers groups.

The paper is organized as follows: Section 2 describes the data collection and data analysis

including the latent class model. Results are displayed in section 3, and the final section provides

the concluding remarks and policy implications.

2. MATERIALS AND METHODS

2.1. Data collection

Farmers’ preferences for which type of contract farming depends on several contract attributes such

as the type of contractor, technical assistance, duration of the contract and pricing mechanism

(Schlecht and Spiller, 2012) For a better qualitative understanding of which behavioral preferences

might affect the valuation of contract characteristics among Omani farmers, focus group

discussions were guided by the theory of transaction costs. Farmers in choosing a contract seek to

lower the transaction costs associated with information, negotiation, and monitoring (North, 1990).

This has helped in evaluating the relevance of specific contract features for farmers and decide how

to design the attributes and which levels to include in the Discrete Choice Experiment. Based on

farmers’ qualitative statements, six attributes were selected. These are shown in Table 1 and vary

systematically in their levels: Type of Partner, Cropping decision rights, quality specifications,

technical assistance, length of the contract, and the price. The levels corresponding to each attribute

of the contract were coded as binary variables except with the price. The level of price specification

was considered based on historical data obtained from Al Batinah farmers’ organization records.

The estimated parameters represent the respondent’s preferences concerning the baseline level.

Indicated (0) represents the reference/baseline for each level.

A seventh variable “None” (NONE) was also considered to estimate respondents’ preferences for

the status-quo option. An example of a card where option four is added to give the farmer the

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freedom and avoid the biases of forcing him/her to choose only from the listed options is displayed

in Appendix A.

Table 1: Summary of contract attributes and levels

No Attributes Levels

1 Type of Partner Retailing firm (0), Farmers’ organization(1), Processing firm (2)

2 Cropping Decision Rights Farmers’ organization(0), Trading Firm (1), Individually (2)

3 Quality specification Variable quality requirement (0), Minimum quality requirement

(1), High-quality standards (2)

4 Technical assistance Provision of technical assistance by the government (0)

Provision of technical assistance by the buyer firm (1)

5 Length of contract

one year (12 months) (0)

one season (9 months) (1)

two years (24 months) (2)

6 Price

Market price (0)

7% less than the market price (1),

7% more than the market price (2),

The data were collected interviewing vegetable producers who were randomly selected from an

already prepared list of 220 common vegetable small-scale farmers’ names obtained from the

Ministry of Agriculture and Fisheries, whose farms with a holding size of fewer than five hectares.

In total 160 farmers were surveyed and interviewed between May and July 2019 in 8 provinces

(wilayat) in Al Dakiliah and Batinah Governorates of the Sultanate of Oman, considered as the

principal vegetable production areas in the country, using a structured questionnaire comprising

two parts. The first part was used to collect the socio-economic and demographic characteristics of

producers. The second part focuses on the choice experiment. To examine the farmer’s preferences

towards different contract models, Ngene software was used to ensure the main effect of interaction

and to generate the efficient total number of choices set combinations. An efficient design of eight

choice sets was generated and broken into three blocks.

Making these cards readable to the farmers with clear levels and attributes, the choice cards are

translated into the Arabic language. Each farmer was asked to complete 8 choice sets and asked to

choose one option out of the three choices presented in every card and a fourth opt-out alternative.

2.2. Data analysis

The data collected were analysed within a random utility framework (McFadden, 1974). Thus, an

individual n presented with j alternatives at a choice occasion t is expected to choose the alternative

that maximizes his/her utility. Following Lancaster’s concept that any product is a bundle of

attributes (Lancaster, 1966), the utility that an individual n derives from the consumption of a

product is assumed to be equal to the sum of his/her marginal utility for each of the product’s

attributes. Therefore, what a farmer derives from a contract farming package is expected to be

equivalent to the summation of the marginal utilities for each of its attributes. Therefore, the

potential value of utility of an individual i’s associated with a contract farming j is defined as 𝑈𝑖𝑗𝑡 ,

which is particular for each jth contract farming alternative a tth choice occasion. It can be written as

a function of two components: an observable systematic component ( 𝑉𝑖𝑗𝑡 ) and a random

component (𝜀𝑖𝑗𝑡 ), which encompasses the unobservable part. Thus, the utility function can be

written as follows:

𝑈𝑖𝑗𝑡 = 𝑉𝑖𝑗𝑡 + 𝜀𝑖𝑗𝑡 …………………… (1)

Where, 𝑉𝑖𝑗𝑡 represents the systematic utility and 𝜀𝑖𝑗𝑡 is assumed to be independent and identically

distributed for all options in each choice set. The systematic utility (𝑉𝑖𝑗𝑡 ) , expressed as a

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generalized regression function (Ben-Akiva and Boccara, 1995) as a function of various

explanatory variables can be written as:

𝑈𝑖𝑗𝑡 = 𝛽𝑋𝑖𝑗𝑡 + 𝜀𝑖𝑗𝑡 …………………… (2)

𝑈𝑖𝑗𝑡 can be expressed by K attributes 𝑋𝑖𝑗𝑡 that are assessed by parameters specific to respondent

choice 𝜷ijt that is not observable and unknown utility parameter by the concerned researchers.

Where β denotes the K×1 vector of unknown marginal utilities that are associated with the Farming

contract attributes Xijt.

The individual preferences that are not observable are to be considered in the stochastic

component𝜀𝑖𝑗𝑡 , assuming an independently and identically distributed extreme value distribution.

Thereby, 𝜷 denotes the vector of unknown parameters that are associated with the farming contract

attributes Xijt. Individuals’ preferences heterogeneity is tested by a model called the Mixed Logit

Model which was suggested by Train (1998). In the Mixed Logit Model, the independence of

Irrelevant Alternatives is relaxed and the following formula defines the conditional choice

probability that individuals choose an alternative j at a choice occasion t as:

𝑝(𝑗|𝑋𝑖𝑡, 𝛽)= ∏ [exp(X′𝑖𝑡,𝑗𝛽𝑞)

∑ exp(𝛽′X′𝑖𝑘𝑡)

𝐽𝑘=1

]𝑇𝑡=1 …………………… (3)

The unconditional choice probability is the expected value of the logit probability integrated over

all possible values of 𝜷 and weighted by the density of:

𝑝(𝑗|𝑋𝑖𝑡, 𝛺) = ∫ 𝑝(𝑗, 𝑋𝑖𝑡, )𝑓(𝛽|𝛺) ⅆ𝛽𝛽

…………………… (4)

As the expression in the above formula does not have one solution, then simulation methods are

created to resolve this issue. The choice probability has been calculated for each draw. Maximum

Likelihood procedures have been used to estimate the simulated log-likelihood (SLL) and are

calculated as:

𝑆𝐿𝐿 = ∑ ∑ 𝑙𝑛𝑇𝑡=1

𝐼𝑖=1 (

1

𝑅 ∑

exp(𝛽′𝑖𝑋𝑖𝑗𝑡)

∑ exp(𝛽′𝑖𝑋𝑖𝑗𝑡)𝑗𝑘=1

𝑅𝑅=1 ) …………………… (5)

Train (2009) has created a new approach called conditional distributions to calculate individual-

specific distributions. The main reason behind this computation is to obtain sufficient information

on the likely position of each respondent. However, using the conditional distributions approach to

evaluate the heterogeneity of respondents’’ preferences is of limited practical use. The estimation

of the Mixed Logit Model has allowed examining that respondents’ attitudes are highly

heterogeneous. Although this model does not explain the source of the heterogeneity it does control

it and considers the attributes variation across respondents.

Therefore, the focus has been shifted to different alternatives termed latent levels (LCM) to analyze

discrete choice analysis and explain the heterogeneity, Greene and Hensher (2003) LCM captures

the source of heterogeneity in the individual’s preferences by assuming a homogenous preferences

pattern within distinct groups. This model is powerful for decision making and policy-relevant

analysis.

The log-likelihood for respondents in the LCM for discrete choice analysis can be described as:

𝑙𝑛𝑙 = ∑ 𝑙𝑛[∑ 𝐻𝑖𝑞(∏ 𝑃𝑖𝑡|𝑞(𝑖)𝑇𝑖𝑡=1 )𝑄

𝑞=1 ]𝑁𝑖=1 …………………… (6)

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Where Hiq denotes prior probability for class q for individual i. for this study, the form of the prior

probability is a multinomial logit

𝐻𝑖𝑞 =Exp(zi′θq)

∑ exp(𝑧′𝑖𝜃𝑞)𝑄𝑞=1

, 𝑞 = 1, … . . , 𝑄, 𝜃𝑄 = 0 …………………… (7)

Where Zi refers to a set of observable characteristics (e,g socio-demographics such as gender, age,

and income) that enter the model for class membership.

Pitq is the chosen probability that individual i, conditional to belonging to class q (q = 1,…q)

chooses alternative i from a particular set j, comprised of j alternatives, in a particular choice

occasion t, and is represented as:

𝑝𝑖𝑡|𝑞(𝑗) =exp(X′𝑖𝑡,𝑗𝛽𝑞)

∑ exp(X′𝑖𝑡,𝑗𝛽𝑞)𝐽𝑗=1

…………………… (8)

Then beta regression analysis uses the computed respondent specific estimates to profile the

members of each class. Two criteria, which are the Consistent Akaike information Criterion

(CAIC) and the Bayesian Information Criterion (BIC), are used to determine the number of classes.

3. RESULTS AND DISCUSSION

The Mixed Logit Model was estimated by using modified Latin Hypercube Sampling draws along

with 1200 simulations, considering repeated choice situations. All the estimations were conducted

using the R-studio software and Stata software. The results displayed in Table 2 show the latent

class model with 3 classes, fit the data better than the conditional logit based on the CAIC and BIC

Values;

Table 2: Information on the converged latent segment models

Number

of Classes

Log likelihood

at convergence (LL)

Number

of parameters (P)

Number

of respondents (N) CAIC BIC

2 -1097.9 31 116 2374.16 2343.16

3 -1052.0 50 116 2392.48 2342.48

4 -1036.3 69 116 2469.60 2400.60

5 -1005.7 88 116 2517.72 2429.72

CAIC (Consistent Akaike Information Criterion) is calculated using: -2 * LL + (ln(N) + 1) * P

BIC (Bayesian Information Criterion) is calculated using: -2 * LL + ln(N) * P

Results derived from the estimation of the Mixed Logit Model and the LCM are presented in Table

3. The results indicate that the estimated marginal utilities are significant. The positive sign of the

coefficient “Opt-out option” suggests a general preference for the opt-out alternative. It is crucial to

recall those respondents who chose “none” for all choices presented have been excluded. Most of

the standard deviation parameters, which show how the valuation of the entire sample spreads

around the estimated means are significant, indicating that the preferences heterogeneity is

maintained among the sampled farmers. The estimated 7% less than the market price coefficient

statistically significant and is negative, indicating that respondents prefer to choose price running

the market rather than the market price, other attributes constant.

3.1. Types of partner

To improve the efficiency of the agricultural sector, the Omani government has decided to establish

an agribusiness company as an institutional solution to link farmers with the market and to ensure

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the food security (Ministry of Agriculture and Fisheries, 2019), it is targeting large scale farming

contracts (Al-Abdali, 2019). Nevertheless, small-scale farmers can integrate with large scale

farming if they operate collectively through farmers’ organizations. However, the forming of such

an organization in Oman is not supported by a legal setting. It is possible to be established as non-

profit organizations, and this legal form constrains it to function as a profit and commercial entity,

(FAO and SQU, 2018).

The results from the Mixed Logit Model, displayed in Table 3 corroborates the existing legislative

status of the farmer’s organization in Oman where that respondents are indifferent about the type of

partner to contract with the farmer’s organization or the trading firm. Nevertheless, respondents in

class 1 and class 2 shows that they are less likely to favor a farmer’s organization compared to the

trading firm. This result is expected, given that the farmer’s organization can lower transaction

costs (Andersson et al., 2015).

Unsurprisingly, the coefficient of the variable “processing firm” is negative and significant at 1%

for class 1, which means that vegetable farmers are more likely to make a contract with retailing

firms such as supermarkets and hypermarkets instead of processing firm. It could be attributed to

the fact that farmers are familiar with the traders than with the processing firm. Moreover, it could

be explained by the farmer’s perception that the processing firms target large scale farms by which

would harm the small scale business farms. Nevertheless, farmers in class 2 are indifferent to adopt

processing firms concerning the retailing partner, but class 3 is more likely to adopt the processing

firm shows a significant level of 5% with a coefficient of 0.713.

3.2. Cropping decision rights

The agricultural commodity market is described by speculative behavior (Tadesse and Gut-

tormsen, 2011). It was expected that farmers would prefer the firm to take the lead of cropping

decision, to avoid unpredicted price fluctuations and the naïve cropping imitation. However, it was

found that overall, farmers are more likely to prefer individual decisions. The results of MLM

displayed in table 3 show that farmers are more likely to decide individually upon the crop’s type

instead of giving the lead to the farmer’s organization. Moreover, farmers are indifferent to the

trading firm and farmer’s organization to decide for them the crop’s type.

Farmer’s organization is the reference line in eliciting the cropping decision to compare the

farmer’s preferences concerning deciding upon the crop type by other partners such as the

Subsidiary trading company or deciding individually. With the latent class model, it shows a

significant sign of 1% for class 1 that is more likely to adopt the decision of trading firm

concerning farmer’s organization. Class 2 and 3 are indifferent to their preferences upon the

decision of cropping. However, with the latent class model, it showed that class 3 are more likely to

adopt the individual decision compared with class 1, while class 2 is the least class that prefers to

go with the individual decision in cropping.

3.3. Quality specification

Quality standards play a major factor in sustaining a healthy contractual relationship (Dolan et al.,

2000; Berdegué et al., 2005). The prior quality specification is crucial to reduce the pertinent

uncertainty related to the demand of the buying firm which results in lower transaction costs

(Goodhue, 2011). Mixed Logit Model in table 3 shows indifferent preferences over the contract that

requires low-quality specification and a contract that specifies the variable quality as a variable.

However, the preferences with high-quality specifications over the variable quality one are highly

significant. With latent class Model, positive and significant results at 5% presented reveal that

farmers in class 2 are more likely to prefer low quality over the variable quality specification and

farmers in class 1 prefer more variable quality specifications.

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In class 2 and 3, they prefer more high-quality specifications with respect to the variable

requirement. This shows that high-quality standards are important for vegetable farmers especially

farmers in class three compared with class 2. On the contrary, farmers in class 1 are indifferent

about the high-quality standard.

3.4. Technical assistance provision

Farmers should have sufficient knowledge and know-how to meet food quality specifications and

enhance productivity. Within the Omani context, farmers can obtain technical assistance from the

government through the Ministry of Agriculture and Fisheries. The statistical analysis has shown a

negative coefficient sign with respect to the technical assistance provided by the government

compared to the buyer firm and this implies that farmers prefer more the assistance to be provided

by the firm instead of the government especially class 1 and class 3. Whereas, class 2 is indifferent

about the source of technical assistance provision. The tendency to favor the provision of technical

assistance by the contracted firms rather than the government can be attributed to the professional

quality approach set by the firm to be followed and found that this professionalism doesn’t exist

with the government (Bellemare, 2010).

According to Masakure and Henson (2005), the technical assistance provided by the buying firm

incentivizes farmers to enhance their farm performance and farmers’ accumulative farming

experience.

3.5. Duration of the contract

Overall, 76% of farmers are indifferent between the three proposed durations of contract. This

implies that farmers prefer the short-duration contract of 9 months compared to 12 months (the

baseline). This tendency towards shorter contracts could be attributed to the fact of farmer’s

uncertainty and risk aversion and could be also explained by the climate adaptability to grow

vegetables during one season only. Short contracts allow the farmers to abrogate the contract if the

partner does not adhere to an agreement. At the same time, other farmers are indifferent about the

length of the contract. The variable “contract of 9 months” coefficient is significant for latent class

3 where the marginal effects are estimated at 0.970 and represent only 23% of sampled farmers.

The other level of contract length “contract 24 months” in the Mixed logit model reveals farmer’s

indifferences about this contract with respect to “contract of 12 months”. This confirms the

preferences by latent class model, the three classes are indifferent about the length of the contract of

24 months with respect to 12 months.

3.6. Price

Price is considered as the first term discussed between parties in contracts( Hernández et al., 2007).

The coefficient of “7% less than market price” is significant and negative. Not surprising, this

revealed that contracts with market prices increase the probability for farmers to adopt a contract.

Market prices guarantee the market for their production (Michelson, 2012). Besides, the market

price reduces the uncertainty associated with fluctuated prices. The same attribute’s coefficient is

negative and significant with latent class 2 that reveals the preference of market price. The level of

7% more than market price shows a coefficient is insignificant with the Latent class model and

Mixed Logit Model, which indicates their indifferences about this level.

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Table 3: Estimated farmers’ preferences

Mixed logit model

(MLM) Latent class model (LCM)

Variables Mean standard

deviations Class 1 Class 2 Class 3

Farmer organization 0.083 -- -0.012 -0.362 0.258

Processing firm -0.492** 1.109*** -0.812*** 0.008 0.713**

Cropping decision by a

Trading firm -0.091 1.142*** 0.758*** 0.222 0.030

Individual cropping decision 0.433* 0.876*** 0.064 -0.262 0.367

Low quality 0.143 0.846** -0.423 0.852** -0.662

High quality 0.589*** 0.648*** 0.370 0.409* 0.824***

Technical assistance by

government -0.209 -- -0.558** 0.201 -0.730***

Contract 9 months 0.098 -- 0.390 0.122 0.970**

Contract 24 months -0.086 -- 0.117 0.144 0.307

7% less than market price -0.538*** -- -0.333 -0.572** -0.506

7% more than the market

price -0.096 -- 0.300 0.046 -0.758

Opt-out option 3.268*** 12.147*** 1.545*** -1.665*** -1.985**

Class shares 1

0.45 0.31 0.23

***, **, and * denotes significant at 1%, 5%, and 10% level of significance respectively.

Table 4: Regression results based on marginal effects

Variables Class 1 Class 2 Class 3

Coef. Std.Err Coef. Std.Err Coef. Std.Err

Region -1.937*** 0.049 0.461*** 0.093 10.4*** 0.324

Gender -0.146** 0.067 -0.046 0.107 0.006 0.008

Low education 0.623*** 0.126 -0.908*** 0.136 0.313*** 0.038

High education 0.706*** 0.057 0.371*** 0.097 -1.681*** 0.008

Age 0.004* 0.002 0.008*** 0.003 0.001* 0.000

Family size -0.012* 0.007 -0.033*** 0.011 -0.002** 0.001

Main job 0.154*** 0.045 0.321*** 0.069 0.015 0.011

Low income 1.093*** 0.057 0.015 0.099 -1.983*** 0.012

High income -0.273*** 0.058 0.204* 0.118 -0.005 0.011

Marketing experience -0.004** 0.002 -0.008*** 0.003 0.000 0.000

Land ownership 0.068 0.053 0.085 0.098 -0.015* 0.008

Medium operated land -0.051 -0.850 -0.017 -0.170 -0.005 -0.690

Large operated land 0.101* 1.880 0.209*** 2.440 0.015 1.370

Inherited land 0.138** 2.110 0.109 1.030 0.002 0.180

Rented land 0.393*** 0.105 0.669*** 0.178 0.033 0.025

Shared land 0.371*** 0.104 0.589*** 0.202 0.029 0.022

Government land 0.613*** 14.280 -0.128* -1.790 -0.715*** -80.110

Low cultivated vegetables 0.038 0.044 0.014 0.074 -0.006 0.008

High cultivated vegetables 0.011 0.077 -0.057 0.160 -0.035 0.017

Constant -0.168 0.171 -1.401*** 0.295 -9.642 0.327

Class share 0.45 0.31 0.23

Log-likelihood final 221.52 141.26 833.42

Numberof observation 111 111 111

Wald test value 11763.3 243.88 729812.5

P-value <0.01 <0.01 <0.01

***, **, and * denotes significant at 1%, 5%, and 10% level of significance respectively.

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330

3.7. Heterogeneity

To gain more information about the hypothesis of this research that one size of the contract does

not fit all, it was necessary to identify different segments with similar preferences. The estimated

standard deviations are significant and indicating the presence of heterogeneity which corroborates

the hypothesis of this research. Moreover, identifying the segments of farmers with homogeneous

preferences would help to efficiently target them using strategies tailored to their specific needs.

Hence, the abovementioned three Latent class models described how farmers’ responses varied

across different segments. In this section, the features that describe the preferences of those groups

are profiled and labeled according to the characteristics of the farmers by using the results of Beta

Regressions displayed in table 4. The variables are described in Appendix B. Beta Regression

analysis is used to determine the relationship between the probability of a farmer to belong to one

of the three classes and the independent variables considered in the regression. This can help design

and tailor the policy that fits the needs of the members of each class and identify the contract model

type which fits the group. Each described class is provided with a title that is compatible with the

contract type. The Probability Scale of the socio-demographic variables presented in Table 4 helps

understand how different classes vary against these indicators and what are the socio-demographic

characteristics of each class.

Table 5: Summary of the preferred contracts by the members of each class

Attributes Class 1

“Multipartite model”

Class 2

“Informal model”

Class 3

“Centralized model”

Type of Partner Retailing Firms indifferent Processing firm

Cropping Decision Trading firm Indifferent Indifferent

Quality specification Indifferent low quality High quality

Technical assistance Trading firm Indifferent Trading firm

Length of contract Indifferent Indifferent 9 months

Price Indifferent Market price Indifferent

Class 1 “Multipartite Model”

This class represents 45% of the entire respondents, and It is the class whose members are

indifferent about farmers’ organization. They are also unwilling to contract with processing firms

and prefer retailing firms instead. To cope with uncertainty and to reduce transaction costs, farmers

in this class are willing to be led by the trading firm in deciding upon the crop types. This group is

also indifferent about the quality specification. This class is more inclined towards seeking the

technical assistance provided by the buying firm. This class preferences match with the attributes of

the multipartite model in terms of the needed coordination and the joint venture business model.

The results displayed in Table 5 show that the Dakhiliya region farmers are more in class 1

compared to the Batinah farmers. Moreover, in this class older people, large family size, and those

whose main job is farming are presented comparing with other demographic segments. In addition,

this class indicates that those of low income are well ahead of those characterized as high income.

Also, this class includes more of those of high marketing experience and those who own highly

operated farms. Farmers who attained land from the government and who inherited their lands are

more in this class.

Class 2 “Informal contract model” This class represents 31 % of the respondents. This type of contract is characterized by its transitory

nature where the farmers are not usually engaged in directed farming.

It is characterized by their indifferent preferences between retailing firms, the farmers’ organization

and the processing firm. They are also indifferent about the decision of selecting the crop. Farmers

in this class prefer low-quality standards. With regards to the provided technical assistance, results

in Table 3 show that farmers are indifferent about it whether to be provided by the government or

the buying firm. The length of the contract is also not crucial to the farmers as the price.

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331

Unsurprisingly, Farmers prefer the “market price” more than the 7% lower price”. The class

preferences match the informal contract model type. From Table 4, it can be noticed that this

member of class 2 is more likely to be located in the Batinah region compared to Dakhliah. The

same segment is more likely to have highly educated farmers and less likely to have a large family

size. In this segment, the farmers whose main job is farming is proportionally higher compared to

other classes. The farm is larger compared to other classes’ farms. Farm ownership indicates that

this group has less governmental land and more rented or shared private land. The marketing

experience of this group shows the negative value and this indicates the low experience the farmers

have in terms of several years.

Class 3 “Centralized contracting model”

This model main characteristics are directed contract farming where there is a commitment from

the Private corporate sector to provide technical assistance to farmers to meet the high-quality

standards. This group represents 23% of the observations. Farmers are indifferent about contracting

with the farmers’ organization or the retailing firm. However, the segment has stronger preferences

to contract with the processing firm. The members of this class are indifferent about the production

decision. The segment also prefers the high-quality standards significantly with respect to the

variable quality specification. The technical assistance provided by the buying firm is much more

preferred compared to the one provided by the government. The members of this segment prefer

the shortest contract of 9 months. These class preferences attribute match more with the centralized

contracting model type, quality specification and technical assistance. Farmers of this class tend to

have low education levels, old age, and medium-income and tend to own their land.

4. CONCLUSIONS

Contract farming is important to solve farmers' marketing problems in Oman. However, there exists

no “one size fits all” contract; farmers exhibit different preferences for partners’ types and terms of

the contract. This study showed that 76% of the farmers prefer contracting with retailing firms and

farmer organizations while 24% of farmers believe that the centralized contract model is their

choice and the processing firm which is supported by the government is the main partner for them.

Policymakers should ensure prospected policies are matched with the farmer’s preferences in

setting legal profit-oriented producers’ organizations.

Funding: This study received a financial support.

Competing Interests: The authors declared that they have no conflict of interests.

Contributors/Acknowledgement: This project is supported by the internal grant (IG/AGR/ECON/18/01)

and from Sultan Qaboos University bench fees given to PhDs students. This work also would not have

been finalized without the assistance of Dr. Faical Akaichi from Scotland’s Rural College at Edinburgh

University. Views and opinions expressed in this study are the views and opinions of the authors, Asian Journal of

Agriculture and Rural Development shall not be responsible or answerable for any loss, damage or

liability, etc. caused in relation to/arising out of the use of the content.

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Appendices

Appendix A: Choice cards “Sample”

Attributes Option 1 Option 2 Option 3 Option 4

Type of Partner Farmer’s

organization Processing Firm Retailing firm

None

of

these

Cropping Decision Rights Farmer’s

organization

Farmer’s

organization Individually

Quality specification

Variable

quality

requirement

Highly quality

standards

Minimum quality

requirement

Technical assistance Provision by

the buyer firm

Provision by the

buyer firm

Provision by the

government

Length of contract Duration of

contract 1 year

Duration of

contract 2 years

Duration of contract

one season

Price Market price 7% more than

market price

7% less than market

price

please put tick on your

choice

Appendix B: Description of beta regression variables

Variable Description

Region 0 = if the farmer is from the Al Batinah Region and, 1= if the farmer is from

AlDakiliha ( interior region )

Gender 0 = if farmers is female, 1 = if the farmer is male.

Low education This variable is coded if 1 the farmer education attainment level is primary,

intermediary, otherwise is 0

Medium education This variable is coded 1 if the farmer education attainment level is

secondary or diploma, otherwise is 0

High education This variable is coded 1 if the farmer reveals a high education of university

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degrees and above.

Age This variable is continuous and indicates the farmer’s age in years.

Family size This variable is continuous and represents how many children the farmer

has.

Main job This variable is coded as = 1 if the main job is farming, otherwise is = 0.

Low income This variable is coded as 1 if the income range between 100 to 600,

otherwise is 0

Medium income This variable is coded as 1 if the income range between 600- 2000,

otherwise is 0

High income This variable is coded as 1 if the farmer‘s income is higher than 2000 OMR.

Marketing

experience

This variable is continuous to represent how many experience years the

farmer has in marketing.

Land ownership This variable is coded if farmers own his land as 1, otherwise is coded as 0

Low operated land This variable is coded if the operated land range is less than 5.0, otherwise

is = 0

Medium operated

land

This variable is coded if the operated land range is between 5.0 –19.9

Feddan, otherwise is = 0

Large operated land This variable is coded as 1 if the operated land Large >= 20.0 Feddan,

otherwise is = 0

Inherited land This variable is coded as 1 if the land is inherited, otherwise is 0

Rented land This variable is coded as 1 if the land is rented, otherwise is 0

Shared land This variable is coded as 1 if the land is shared with another partner,

otherwise is 0

Purchased land This variable is coded as 1 if the land is purchased, otherwise is 0

Government land This variable is coded as 1 if the government grants the land, otherwise is 0

Low cultivated

vegetables This variable is coded as 1 if the cultivated vegetables is < 5.0 ton

High cultivated

vegetable This variable is coded as 1 if the cultivated vegetables is < 20 ton